{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fully-Connected Neural Nets\n",
    "In the previous homework you implemented a fully-connected two-layer neural network on CIFAR-10. The implementation was simple but not very modular since the loss and gradient were computed in a single monolithic function. This is manageable for a simple two-layer network, but would become impractical as we move to bigger models. Ideally we want to build networks using a more modular design so that we can implement different layer types in isolation and then snap them together into models with different architectures.\n",
    "\n",
    "In this exercise we will implement fully-connected networks using a more modular approach. For each layer we will implement a `forward` and a `backward` function. The `forward` function will receive inputs, weights, and other parameters and will return both an output and a `cache` object storing data needed for the backward pass, like this:\n",
    "\n",
    "```python\n",
    "def layer_forward(x, w):\n",
    "  \"\"\" Receive inputs x and weights w \"\"\"\n",
    "  # Do some computations ...\n",
    "  z = # ... some intermediate value\n",
    "  # Do some more computations ...\n",
    "  out = # the output\n",
    "   \n",
    "  cache = (x, w, z, out) # Values we need to compute gradients\n",
    "   \n",
    "  return out, cache\n",
    "```\n",
    "\n",
    "The backward pass will receive upstream derivatives and the `cache` object, and will return gradients with respect to the inputs and weights, like this:\n",
    "\n",
    "```python\n",
    "def layer_backward(dout, cache):\n",
    "  \"\"\"\n",
    "  Receive derivative of loss with respect to outputs and cache,\n",
    "  and compute derivative with respect to inputs.\n",
    "  \"\"\"\n",
    "  # Unpack cache values\n",
    "  x, w, z, out = cache\n",
    "  \n",
    "  # Use values in cache to compute derivatives\n",
    "  dx = # Derivative of loss with respect to x\n",
    "  dw = # Derivative of loss with respect to w\n",
    "  \n",
    "  return dx, dw\n",
    "```\n",
    "\n",
    "After implementing a bunch of layers this way, we will be able to easily combine them to build classifiers with different architectures.\n",
    "\n",
    "In addition to implementing fully-connected networks of arbitrary depth, we will also explore different update rules for optimization, and introduce Dropout as a regularizer and Batch Normalization as a tool to more efficiently optimize deep networks.\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "run the following from the cs231n directory and try again:\n",
      "python setup.py build_ext --inplace\n",
      "You may also need to restart your iPython kernel\n"
     ]
    }
   ],
   "source": [
    "# As usual, a bit of setup\n",
    "\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.fc_net import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "  \"\"\" returns relative error \"\"\"\n",
    "  return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train:  (49000, 3, 32, 32)\n",
      "y_train:  (49000,)\n",
      "X_val:  (1000, 3, 32, 32)\n",
      "y_val:  (1000,)\n",
      "X_test:  (1000, 3, 32, 32)\n",
      "y_test:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in data.items():\n",
    "  print ('%s: ' % k, v.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Affine layer: foward\n",
    "Open the file `cs231n/layers.py` and implement the `affine_forward` function.\n",
    "\n",
    "Once you are done you can test your implementaion by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_forward function:\n",
      "difference:  9.769847728806635e-10\n"
     ]
    }
   ],
   "source": [
    "# Test the affine_forward function\n",
    "\n",
    "num_inputs = 2\n",
    "input_shape = (4, 5, 6)\n",
    "output_dim = 3\n",
    "\n",
    "input_size = num_inputs * np.prod(input_shape)\n",
    "weight_size = output_dim * np.prod(input_shape)\n",
    "\n",
    "x = np.linspace(-0.1, 0.5, num=input_size).reshape(num_inputs, *input_shape)\n",
    "w = np.linspace(-0.2, 0.3, num=weight_size).reshape(np.prod(input_shape), output_dim)\n",
    "b = np.linspace(-0.3, 0.1, num=output_dim)\n",
    "\n",
    "out, _ = affine_forward(x, w, b)\n",
    "correct_out = np.array([[ 1.49834967,  1.70660132,  1.91485297],\n",
    "                        [ 3.25553199,  3.5141327,   3.77273342]])\n",
    "\n",
    "# Compare your output with ours. The error should be around 1e-9.\n",
    "print ('Testing affine_forward function:')\n",
    "print ('difference: ', rel_error(out, correct_out))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Affine layer: backward\n",
    "Now implement the `affine_backward` function and test your implementation using numeric gradient checking."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_backward function:\n",
      "dx error:  6.952532991948393e-10\n",
      "dw error:  5.918588668962945e-11\n",
      "db error:  2.764834770450656e-11\n"
     ]
    }
   ],
   "source": [
    "# Test the affine_backward function\n",
    "\n",
    "x = np.random.randn(10, 2, 3)\n",
    "w = np.random.randn(6, 5)\n",
    "b = np.random.randn(5)\n",
    "dout = np.random.randn(10, 5)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: affine_forward(x, w, b)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: affine_forward(x, w, b)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: affine_forward(x, w, b)[0], b, dout)\n",
    "\n",
    "_, cache = affine_forward(x, w, b)\n",
    "dx, dw, db = affine_backward(dout, cache)\n",
    "\n",
    "# The error should be around 1e-10\n",
    "print ('Testing affine_backward function:')\n",
    "print ('dx error: ', rel_error(dx_num, dx))\n",
    "print ('dw error: ', rel_error(dw_num, dw))\n",
    "print ('db error: ', rel_error(db_num, db))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ReLU layer: forward\n",
    "Implement the forward pass for the ReLU activation function in the `relu_forward` function and test your implementation using the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing relu_forward function:\n",
      "difference:  4.999999798022158e-08\n"
     ]
    }
   ],
   "source": [
    "# Test the relu_forward function\n",
    "\n",
    "x = np.linspace(-0.5, 0.5, num=12).reshape(3, 4)\n",
    "\n",
    "out, _ = relu_forward(x)\n",
    "correct_out = np.array([[ 0.,          0.,          0.,          0.,        ],\n",
    "                        [ 0.,          0.,          0.04545455,  0.13636364,],\n",
    "                        [ 0.22727273,  0.31818182,  0.40909091,  0.5,       ]])\n",
    "\n",
    "# Compare your output with ours. The error should be around 1e-8\n",
    "print ('Testing relu_forward function:')\n",
    "print ('difference: ', rel_error(out, correct_out))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ReLU layer: backward\n",
    "Now implement the backward pass for the ReLU activation function in the `relu_backward` function and test your implementation using numeric gradient checking:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing relu_backward function:\n",
      "dx error:  3.275627662805467e-12\n"
     ]
    }
   ],
   "source": [
    "x = np.random.randn(10, 10)\n",
    "dout = np.random.randn(*x.shape)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: relu_forward(x)[0], x, dout)\n",
    "\n",
    "_, cache = relu_forward(x)\n",
    "dx = relu_backward(dout, cache)\n",
    "\n",
    "# The error should be around 1e-12\n",
    "print ('Testing relu_backward function:')\n",
    "print ('dx error: ', rel_error(dx_num, dx))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# \"Sandwich\" layers\n",
    "There are some common patterns of layers that are frequently used in neural nets. For example, affine layers are frequently followed by a ReLU nonlinearity. To make these common patterns easy, we define several convenience layers in the file `cs231n/layer_utils.py`.\n",
    "\n",
    "For now take a look at the `affine_relu_forward` and `affine_relu_backward` functions, and run the following to numerically gradient check the backward pass:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_relu_forward:\n",
      "dx error:  1.0791733068338208e-09\n",
      "dw error:  3.1904324021817884e-10\n",
      "db error:  1.8928823065313853e-11\n"
     ]
    }
   ],
   "source": [
    "from cs231n.layer_utils import affine_relu_forward, affine_relu_backward\n",
    "\n",
    "x = np.random.randn(2, 3, 4)\n",
    "w = np.random.randn(12, 10)\n",
    "b = np.random.randn(10)\n",
    "dout = np.random.randn(2, 10)\n",
    "\n",
    "out, cache = affine_relu_forward(x, w, b)\n",
    "dx, dw, db = affine_relu_backward(dout, cache)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: affine_relu_forward(x, w, b)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: affine_relu_forward(x, w, b)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: affine_relu_forward(x, w, b)[0], b, dout)\n",
    "\n",
    "print ('Testing affine_relu_forward:')\n",
    "print ('dx error: ', rel_error(dx_num, dx))\n",
    "print ('dw error: ', rel_error(dw_num, dw))\n",
    "print ('db error: ', rel_error(db_num, db))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loss layers: Softmax and SVM\n",
    "You implemented these loss functions in the last assignment, so we'll give them to you for free here. You should still make sure you understand how they work by looking at the implementations in `cs231n/layers.py`.\n",
    "\n",
    "You can make sure that the implementations are correct by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing svm_loss:\n",
      "loss:  8.999004324662115\n",
      "dx error:  1.4021566006651672e-09\n",
      "\n",
      "Testing softmax_loss:\n",
      "loss:  2.3024859550887125\n",
      "dx error:  9.55072032126894e-09\n"
     ]
    }
   ],
   "source": [
    "num_classes, num_inputs = 10, 50\n",
    "x = 0.001 * np.random.randn(num_inputs, num_classes)\n",
    "y = np.random.randint(num_classes, size=num_inputs)\n",
    "\n",
    "dx_num = eval_numerical_gradient(lambda x: svm_loss(x, y)[0], x, verbose=False)\n",
    "loss, dx = svm_loss(x, y)\n",
    "\n",
    "# Test svm_loss function. Loss should be around 9 and dx error should be 1e-9\n",
    "print ('Testing svm_loss:')\n",
    "print ('loss: ', loss)\n",
    "print ('dx error: ', rel_error(dx_num, dx))\n",
    "\n",
    "dx_num = eval_numerical_gradient(lambda x: softmax_loss(x, y)[0], x, verbose=False)\n",
    "loss, dx = softmax_loss(x, y)\n",
    "\n",
    "# Test softmax_loss function. Loss should be 2.3 and dx error should be 1e-8\n",
    "print ('\\nTesting softmax_loss:')\n",
    "print ('loss: ', loss)\n",
    "print ('dx error: ', rel_error(dx_num, dx))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Two-layer network\n",
    "In the previous assignment you implemented a two-layer neural network in a single monolithic class. Now that you have implemented modular versions of the necessary layers, you will reimplement the two layer network using these modular implementations.\n",
    "\n",
    "Open the file `cs231n/classifiers/fc_net.py` and complete the implementation of the `TwoLayerNet` class. This class will serve as a model for the other networks you will implement in this assignment, so read through it to make sure you understand the API. You can run the cell below to test your implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing initialization ... \n",
      "Testing test-time forward pass ... \n",
      "Testing training loss (no regularization)\n",
      "Running numeric gradient check with reg =  0.0\n",
      "W1 relative error: 1.52e-08\n",
      "W2 relative error: 3.48e-10\n",
      "b1 relative error: 6.55e-09\n",
      "b2 relative error: 4.33e-10\n",
      "Running numeric gradient check with reg =  0.7\n",
      "W1 relative error: 8.18e-07\n",
      "W2 relative error: 7.98e-08\n",
      "b1 relative error: 1.09e-09\n",
      "b2 relative error: 9.09e-10\n"
     ]
    }
   ],
   "source": [
    "N, D, H, C = 3, 5, 50, 7\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=N)\n",
    "\n",
    "std = 1e-2\n",
    "model = TwoLayerNet(input_dim=D, hidden_dim=H, num_classes=C, weight_scale=std)\n",
    "\n",
    "print ('Testing initialization ... ')\n",
    "W1_std = abs(model.params['W1'].std() - std)\n",
    "b1 = model.params['b1']\n",
    "W2_std = abs(model.params['W2'].std() - std)\n",
    "b2 = model.params['b2']\n",
    "assert W1_std < std / 10, 'First layer weights do not seem right'\n",
    "assert np.all(b1 == 0), 'First layer biases do not seem right'\n",
    "assert W2_std < std / 10, 'Second layer weights do not seem right'\n",
    "assert np.all(b2 == 0), 'Second layer biases do not seem right'\n",
    "\n",
    "print ('Testing test-time forward pass ... ')\n",
    "model.params['W1'] = np.linspace(-0.7, 0.3, num=D*H).reshape(D, H)\n",
    "model.params['b1'] = np.linspace(-0.1, 0.9, num=H)\n",
    "model.params['W2'] = np.linspace(-0.3, 0.4, num=H*C).reshape(H, C)\n",
    "model.params['b2'] = np.linspace(-0.9, 0.1, num=C)\n",
    "X = np.linspace(-5.5, 4.5, num=N*D).reshape(D, N).T\n",
    "scores = model.loss(X)\n",
    "correct_scores = np.asarray(\n",
    "  [[11.53165108,  12.2917344,   13.05181771,  13.81190102,  14.57198434, 15.33206765,  16.09215096],\n",
    "   [12.05769098,  12.74614105,  13.43459113,  14.1230412,   14.81149128, 15.49994135,  16.18839143],\n",
    "   [12.58373087,  13.20054771,  13.81736455,  14.43418138,  15.05099822, 15.66781506,  16.2846319 ]])\n",
    "scores_diff = np.abs(scores - correct_scores).sum()\n",
    "assert scores_diff < 1e-6, 'Problem with test-time forward pass'\n",
    "\n",
    "print ('Testing training loss (no regularization)')\n",
    "y = np.asarray([0, 5, 1])\n",
    "loss, grads = model.loss(X, y)\n",
    "correct_loss = 3.4702243556\n",
    "assert abs(loss - correct_loss) < 1e-10, 'Problem with training-time loss'\n",
    "\n",
    "model.reg = 1.0\n",
    "loss, grads = model.loss(X, y)\n",
    "correct_loss = 26.5948426952\n",
    "assert abs(loss - correct_loss) < 1e-10, 'Problem with regularization loss'\n",
    "\n",
    "for reg in [0.0, 0.7]:\n",
    "  print ('Running numeric gradient check with reg = ', reg)\n",
    "  model.reg = reg\n",
    "  loss, grads = model.loss(X, y)\n",
    "\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False)\n",
    "    print ('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Solver\n",
    "In the previous assignment, the logic for training models was coupled to the models themselves. Following a more modular design, for this assignment we have split the logic for training models into a separate class.\n",
    "\n",
    "Open the file `cs231n/solver.py` and read through it to familiarize yourself with the API. After doing so, use a `Solver` instance to train a `TwoLayerNet` that achieves at least `50%` accuracy on the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 4900) loss: 2.302478\n",
      "(Epoch 0 / 10) train acc: 0.130000; val_acc: 0.113000\n",
      "(Iteration 101 / 4900) loss: 1.833818\n",
      "(Iteration 201 / 4900) loss: 1.579096\n",
      "(Iteration 301 / 4900) loss: 1.740128\n",
      "(Iteration 401 / 4900) loss: 1.468408\n",
      "(Epoch 1 / 10) train acc: 0.459000; val_acc: 0.459000\n",
      "(Iteration 501 / 4900) loss: 1.583155\n",
      "(Iteration 601 / 4900) loss: 1.451746\n",
      "(Iteration 701 / 4900) loss: 1.559063\n",
      "(Iteration 801 / 4900) loss: 1.589088\n",
      "(Iteration 901 / 4900) loss: 1.476324\n",
      "(Epoch 2 / 10) train acc: 0.497000; val_acc: 0.470000\n",
      "(Iteration 1001 / 4900) loss: 1.415363\n",
      "(Iteration 1101 / 4900) loss: 1.465780\n",
      "(Iteration 1201 / 4900) loss: 1.467563\n",
      "(Iteration 1301 / 4900) loss: 1.529872\n",
      "(Iteration 1401 / 4900) loss: 1.241146\n",
      "(Epoch 3 / 10) train acc: 0.525000; val_acc: 0.465000\n",
      "(Iteration 1501 / 4900) loss: 1.352048\n",
      "(Iteration 1601 / 4900) loss: 1.309299\n",
      "(Iteration 1701 / 4900) loss: 1.563440\n",
      "(Iteration 1801 / 4900) loss: 1.325163\n",
      "(Iteration 1901 / 4900) loss: 1.466014\n",
      "(Epoch 4 / 10) train acc: 0.562000; val_acc: 0.497000\n",
      "(Iteration 2001 / 4900) loss: 1.404894\n",
      "(Iteration 2101 / 4900) loss: 1.320446\n",
      "(Iteration 2201 / 4900) loss: 1.246114\n",
      "(Iteration 2301 / 4900) loss: 1.163239\n",
      "(Iteration 2401 / 4900) loss: 1.428734\n",
      "(Epoch 5 / 10) train acc: 0.521000; val_acc: 0.498000\n",
      "(Iteration 2501 / 4900) loss: 1.287790\n",
      "(Iteration 2601 / 4900) loss: 1.165026\n",
      "(Iteration 2701 / 4900) loss: 1.341816\n",
      "(Iteration 2801 / 4900) loss: 1.237444\n",
      "(Iteration 2901 / 4900) loss: 1.209145\n",
      "(Epoch 6 / 10) train acc: 0.549000; val_acc: 0.498000\n",
      "(Iteration 3001 / 4900) loss: 1.179000\n",
      "(Iteration 3101 / 4900) loss: 1.117959\n",
      "(Iteration 3201 / 4900) loss: 1.366250\n",
      "(Iteration 3301 / 4900) loss: 1.133212\n",
      "(Iteration 3401 / 4900) loss: 1.259114\n",
      "(Epoch 7 / 10) train acc: 0.581000; val_acc: 0.518000\n",
      "(Iteration 3501 / 4900) loss: 1.215429\n",
      "(Iteration 3601 / 4900) loss: 1.126893\n",
      "(Iteration 3701 / 4900) loss: 0.913066\n",
      "(Iteration 3801 / 4900) loss: 1.222091\n",
      "(Iteration 3901 / 4900) loss: 1.349819\n",
      "(Epoch 8 / 10) train acc: 0.588000; val_acc: 0.504000\n",
      "(Iteration 4001 / 4900) loss: 1.338464\n",
      "(Iteration 4101 / 4900) loss: 1.243075\n",
      "(Iteration 4201 / 4900) loss: 1.319333\n",
      "(Iteration 4301 / 4900) loss: 0.931233\n",
      "(Iteration 4401 / 4900) loss: 1.298143\n",
      "(Epoch 9 / 10) train acc: 0.598000; val_acc: 0.503000\n",
      "(Iteration 4501 / 4900) loss: 1.068071\n",
      "(Iteration 4601 / 4900) loss: 1.027317\n",
      "(Iteration 4701 / 4900) loss: 1.175973\n",
      "(Iteration 4801 / 4900) loss: 1.003193\n",
      "(Epoch 10 / 10) train acc: 0.625000; val_acc: 0.530000\n"
     ]
    }
   ],
   "source": [
    "model = TwoLayerNet()\n",
    "solver = None\n",
    "\n",
    "##############################################################################\n",
    "# TODO: Use a Solver instance to train a TwoLayerNet that achieves at least  #\n",
    "# 50% accuracy on the validation set.                                        #\n",
    "##############################################################################\n",
    "pass\n",
    "solver = Solver(model, data,\n",
    "                  update_rule='sgd',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                  },\n",
    "                  lr_decay=0.95,\n",
    "                  num_epochs=10, batch_size=100,\n",
    "                  print_every=100)\n",
    "solver.train()\n",
    "##############################################################################\n",
    "#                             END OF YOUR CODE                               #\n",
    "##############################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run this cell to visualize training loss and train / val accuracy\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.title('Accuracy')\n",
    "plt.plot(solver.train_acc_history, '-o', label='train')\n",
    "plt.plot(solver.val_acc_history, '-o', label='val')\n",
    "plt.plot([0.5] * len(solver.val_acc_history), 'k--')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(loc='lower right')\n",
    "plt.gcf().set_size_inches(15, 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multilayer network\n",
    "Next you will implement a fully-connected network with an arbitrary number of hidden layers.\n",
    "\n",
    "Read through the `FullyConnectedNet` class in the file `cs231n/classifiers/fc_net.py`.\n",
    "\n",
    "Implement the initialization, the forward pass, and the backward pass. For the moment don't worry about implementing dropout or batch normalization; we will add those features soon."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial loss and gradient check"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a sanity check, run the following to check the initial loss and to gradient check the network both with and without regularization. Do the initial losses seem reasonable?\n",
    "\n",
    "For gradient checking, you should expect to see errors around 1e-6 or less."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running check with reg =  0\n",
      "Initial loss:  2.299800727824695\n",
      "W1 relative error: 2.94e-07\n",
      "W2 relative error: 8.19e-08\n",
      "W3 relative error: 6.70e-08\n",
      "b1 relative error: 5.96e-09\n",
      "b2 relative error: 1.45e-09\n",
      "b3 relative error: 1.05e-10\n",
      "Running check with reg =  3.14\n",
      "Initial loss:  7.145076820351696\n",
      "W1 relative error: 1.54e-08\n",
      "W2 relative error: 2.94e-08\n",
      "W3 relative error: 4.92e-08\n",
      "b1 relative error: 3.89e-08\n",
      "b2 relative error: 2.31e-09\n",
      "b3 relative error: 3.64e-10\n"
     ]
    }
   ],
   "source": [
    "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=(N,))\n",
    "\n",
    "for reg in [0, 3.14]:\n",
    "  print ('Running check with reg = ', reg)\n",
    "  model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n",
    "                            reg=reg, weight_scale=5e-2, dtype=np.float64)\n",
    "\n",
    "  loss, grads = model.loss(X, y)\n",
    "  print ('Initial loss: ', loss)\n",
    "\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n",
    "    print ('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As another sanity check, make sure you can overfit a small dataset of 50 images. First we will try a three-layer network with 100 units in each hidden layer. You will need to tweak the learning rate and initialization scale, but you should be able to overfit and achieve 100% training accuracy within 20 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 40) loss: 2.248936\n",
      "(Epoch 0 / 20) train acc: 0.280000; val_acc: 0.125000\n",
      "(Epoch 1 / 20) train acc: 0.360000; val_acc: 0.093000\n",
      "(Epoch 2 / 20) train acc: 0.320000; val_acc: 0.115000\n",
      "(Epoch 3 / 20) train acc: 0.600000; val_acc: 0.150000\n",
      "(Epoch 4 / 20) train acc: 0.600000; val_acc: 0.170000\n",
      "(Epoch 5 / 20) train acc: 0.640000; val_acc: 0.160000\n",
      "(Iteration 11 / 40) loss: 1.324614\n",
      "(Epoch 6 / 20) train acc: 0.820000; val_acc: 0.167000\n",
      "(Epoch 7 / 20) train acc: 0.860000; val_acc: 0.152000\n",
      "(Epoch 8 / 20) train acc: 0.900000; val_acc: 0.157000\n",
      "(Epoch 9 / 20) train acc: 0.820000; val_acc: 0.181000\n",
      "(Epoch 10 / 20) train acc: 0.900000; val_acc: 0.170000\n",
      "(Iteration 21 / 40) loss: 0.442689\n",
      "(Epoch 11 / 20) train acc: 0.940000; val_acc: 0.177000\n",
      "(Epoch 12 / 20) train acc: 0.980000; val_acc: 0.178000\n",
      "(Epoch 13 / 20) train acc: 0.940000; val_acc: 0.185000\n",
      "(Epoch 14 / 20) train acc: 0.940000; val_acc: 0.190000\n",
      "(Epoch 15 / 20) train acc: 0.980000; val_acc: 0.175000\n",
      "(Iteration 31 / 40) loss: 0.238214\n",
      "(Epoch 16 / 20) train acc: 0.980000; val_acc: 0.176000\n",
      "(Epoch 17 / 20) train acc: 1.000000; val_acc: 0.173000\n",
      "(Epoch 18 / 20) train acc: 1.000000; val_acc: 0.178000\n",
      "(Epoch 19 / 20) train acc: 1.000000; val_acc: 0.163000\n",
      "(Epoch 20 / 20) train acc: 1.000000; val_acc: 0.166000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Use a three-layer Net to overfit 50 training examples.\n",
    "\n",
    "num_train = 50\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "weight_scale = 1e-2\n",
    "learning_rate = 8e-3\n",
    "model = FullyConnectedNet([100, 100],\n",
    "              weight_scale=weight_scale, dtype=np.float64)\n",
    "solver = Solver(model, small_data,\n",
    "                print_every=10, num_epochs=20, batch_size=25,\n",
    "                update_rule='sgd',\n",
    "                optim_config={\n",
    "                  'learning_rate': learning_rate,\n",
    "                }\n",
    "         )\n",
    "solver.train()\n",
    "\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.title('Training loss history')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Training loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now try to use a five-layer network with 100 units on each layer to overfit 50 training examples. Again you will have to adjust the learning rate and weight initialization, but you should be able to achieve 100% training accuracy within 20 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 40) loss: 88.874908\n",
      "(Epoch 0 / 20) train acc: 0.260000; val_acc: 0.113000\n",
      "(Epoch 1 / 20) train acc: 0.160000; val_acc: 0.120000\n",
      "(Epoch 2 / 20) train acc: 0.320000; val_acc: 0.122000\n",
      "(Epoch 3 / 20) train acc: 0.500000; val_acc: 0.156000\n",
      "(Epoch 4 / 20) train acc: 0.660000; val_acc: 0.163000\n",
      "(Epoch 5 / 20) train acc: 0.780000; val_acc: 0.147000\n",
      "(Iteration 11 / 40) loss: 2.667402\n",
      "(Epoch 6 / 20) train acc: 0.920000; val_acc: 0.164000\n",
      "(Epoch 7 / 20) train acc: 0.960000; val_acc: 0.155000\n",
      "(Epoch 8 / 20) train acc: 1.000000; val_acc: 0.157000\n",
      "(Epoch 9 / 20) train acc: 1.000000; val_acc: 0.157000\n",
      "(Epoch 10 / 20) train acc: 1.000000; val_acc: 0.157000\n",
      "(Iteration 21 / 40) loss: 0.000002\n",
      "(Epoch 11 / 20) train acc: 1.000000; val_acc: 0.157000\n",
      "(Epoch 12 / 20) train acc: 1.000000; val_acc: 0.157000\n",
      "(Epoch 13 / 20) train acc: 1.000000; val_acc: 0.157000\n",
      "(Epoch 14 / 20) train acc: 1.000000; val_acc: 0.157000\n",
      "(Epoch 15 / 20) train acc: 1.000000; val_acc: 0.157000\n",
      "(Iteration 31 / 40) loss: 0.000038\n",
      "(Epoch 16 / 20) train acc: 1.000000; val_acc: 0.157000\n",
      "(Epoch 17 / 20) train acc: 1.000000; val_acc: 0.157000\n",
      "(Epoch 18 / 20) train acc: 1.000000; val_acc: 0.157000\n",
      "(Epoch 19 / 20) train acc: 1.000000; val_acc: 0.157000\n",
      "(Epoch 20 / 20) train acc: 1.000000; val_acc: 0.157000\n"
     ]
    },
    {
     "data": {
      "image/png": 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3TJLHAC8CHtv1+eMkSwZYmyRJ0sgYWCCrqquAOw5pfi5wSff+EmDdjPb3VNXdVfUV4AbgiYOqTZIkaZQMew7Zw6rqFoDu9aFd+0rgazO22921fY8k65NsT7J97969Ay1WkiRpGEZlUn9maavZNqyqi6tqTVWtWb58+YDLkiRJGrxhB7Jbk5wC0L3e1rXvBk6bsd2pwJ4h1yZJktTEsAPZB4Dzu/fnA++f0f6iJA9MciZwFvCZIdcmSZLUxHGD+uAklwHnAicn2Q28HrgIeF+SlwFfBX4eoKquT/I+4PPAPcCvVNWBQdUmSZI0SgYWyKrqvDlWPW2O7X8b+O1B1TPKtu6YZPO2XezZN8WKZRNsWLuKdatnvaZBkiQtQgMLZOrP1h2TbNyyk6n9vROCk/um2LhlJ4ChTJKkMTEqV1mOrc3bdt0XxqZN7T/A5m27GlUkSZKGzUDW2J59U4fVLkmSFp/7DWRJzklyfPf+JUl+N8nDB1/aeFixbOKw2iVJ0uLTzxmyNwPfSfKjwGuBm4F3DLSqMbJh7Somlh782M6JpUvYsHZVo4okSdKw9RPI7qmqove8yT+oqj8AThxsWeNj3eqVvOH5j2flsgkCrFw2wRue/3gn9EuSNEb6ucryziQbgZcAT0myBFg62LLGy7rVKw1gkiSNsX7OkL0QuBt4WVV9nd5DvzcPtCpJkqQx0tcZMnpDlQeSPAp4NHDZYMuSJEkaH/2cIbsKeGCSlcDHgF8E3j7IoiRJksZJP4EsVfUd4PnA/66q5wGPHWxZkiRJ46OvQJbkScCLgQ91bUvm2V6SJEmHoZ9A9ipgI3BFVV2f5BHAxwdbliRJ0vi430n9VfVJ4JNJTkxyQlXdCPza4EuTJEkaD/08OunxSXYA1wGfT3J1EueQSZIkLZB+hiz/FHh1VT28qk4HXgO8ZbBlSZIkjY9+AtnxVXXfnLGq+gRw/MAqkiRJGjP93Bj2xiT/A3hnt/wS4CuDK0mSJGm89BPIfgm4ENgChN6NYn9xkEWNiq07Jtm8bRd79k2xYtkEG9au8pmTkiRpwfVzleU3GMOrKrfumGTjlp1M7T8AwOS+KTZu2QlgKJMkSQtqzkCW5INAzbW+qp4zkIpGxOZtu+4LY9Om9h9g87ZdBjJJkrSg5jtD9sahVTGC9uybOqx2SZKkIzVnIOtuCDu2ViybYHKW8LVi2USDaiRJ0mLWz20vxtKGtauYWHrwIzsnli5hw9pVjSqSJEmLVT9XWY6l6XliXmUpSZIGzUA2j3WrVxrAJEnSwN1vIJvjastvAtuBP62q7w6iMEmSpHHRzxyyG4G76D2/8i3At4BbgUfhMy0lSZKOWj9Dlqur6ikzlj+Y5KqqekqS6wdVmCRJ0rjo5wzZ8iSnTy9070/uFv95IFVJkiSNkX7OkL0G+FSSL9N7luWZwH9OcjxwySCLkyRJGgf9PMvyw0nOAh5NL5B9ccZE/t8fZHGSJEnjoN/bXjwBOKPb/keSUFXvGFhVkiRJY6Sf2168E3gkcA0w/bTtAgxkkiRJC6CfM2RrgMdU1aH3IpMkSdIC6Ocqy+uAHxp0IZIkSeOqnzNkJwOfT/IZ4O7pxqp6zsCqkiRJGiP9BLJNgy5CkiRpnPVz24tPDqMQSZKkcTVnIEvyqap6cpI7Ofjh4gGqqk4aeHWSJEljYM5AVlVP7l5PHF45kiRJ46evG8MmWQI8bOb2VfXVQRUlSZI0Tvq5MeyvAq8HbgXu7ZoL+JEB1iVJkjQ2+jlD9uvAqqq6fdDFSJIkjaN+bgz7NeCbgy5EkiRpXPVzhuxG4BNJPsTBN4b93YFVJUmSNEb6CWRf7X4e0P1IkiRpAfVzY9gLh1GIJEnSuJrvxrC/X1WvSvJBDr4xLOCzLCVJkhbKfGfI3tm9vnEYhUiSJI2r+e7Uf3X3uqDPskyyCnjvjKZHAL8JLAP+E7C3a39dVX14Ib9bkiRpFPVzY9izgDcAjwG+f7q9qh5xJF9YVbuAs7vPXgJMAlcAvwj8XlV5Rk6SJI2Vfu5D9mfAm4F7gH8HvIN/Gc48Wk8DvlxVNy/Q50mSJB1z+glkE1X1MSBVdXNVbQKeukDf/yLgshnLr0xybZK3JXnwbB2SrE+yPcn2vXv3zraJJEnSMaWfQPbdJN8HfCnJK5M8D3jo0X5xkgcAzwH+vGt6M/BIesOZtwBvmq1fVV1cVWuqas3y5cuPtgxJkqTm+glkrwIeBPwa8ATgJcD5C/DdPwN8rqpuBaiqW6vqQFXdC7wFeOICfIckSdLIm3dSfzfp/gVVtQG4i97E+4VyHjOGK5OcUlW3dIvPA65bwO+SJEkaWfPdGPa4qronyROSpKq+5+awRyrJg4CfAl4+o/l3kpxN7ya0Nx2yTpIkadGa7wzZZ4AfA3YA70/y58C3p1dW1ZYj/dKq+g7wg4e0vfRIP0+SJOlY1s/DxR8C3E7vysoC0r0ecSCTJEnSv5gvkD00yavpzeWaDmLTFmz4UpIkadzNF8iWACdwcBCbZiCTJElaIPMFsluq6reGVokkSdKYmu8+ZLOdGZMkSdICmy+QPW1oVUiSJI2xOQNZVd0xzEIkSZLGVT+PTpIkSdIAGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkho7rsWXJrkJuBM4ANxTVWuSPAR4L3AGcBPwgqr6Rov6JEmShqnlGbJ/V1VnV9WabvkC4GNVdRbwsW5ZkiRp0RulIcvnApd07y8B1jWsRZIkaWhaBbICPpLk6iTru7aHVdUtAN3rQxvVJkmSNFRN5pAB51TVniQPBT6a5Iv9duwC3HqA008/fVD1SZIkDU2TM2RVtad7vQ24AngicGuSUwC619vm6HtxVa2pqjXLly8fVsmSJEkDM/RAluT4JCdOvwd+GrgO+ABwfrfZ+cD7h12bJElSCy2GLB8GXJFk+vvfXVV/neSzwPuSvAz4KvDzDWqTJEkauqEHsqq6EfjRWdpvB5427HokSZJaG6XbXkiSJI0lA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDV2XOsCNDxbd0yyedsu9uybYsWyCTasXcW61StblyVJ0tgzkI2JrTsm2bhlJ1P7DwAwuW+KjVt2AhjKJElqzCHLMbF52677wti0qf0H2LxtV6OKJEnSNAPZmNizb+qw2iVJ0vAYyMbEimUTh9UuSZKGx0A2JjasXcXE0iUHtU0sXcKGtasaVSRJkqY5qX9MTE/c9ypLSZJGj4FsjKxbvdIAJknSCHLIUpIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjQ09kCU5LcnHk3whyfVJfr1r35RkMsk13c8zh12bJElSC8c1+M57gNdU1eeSnAhcneSj3brfq6o3NqhJkiSpmaEHsqq6Bbile39nki8AK4ddhyRJ0qhoOocsyRnAauDvu6ZXJrk2yduSPLhZYZIkSUPULJAlOQG4HHhVVX0LeDPwSOBsemfQ3jRHv/VJtifZvnfv3qHVO6627pjknIuu5MwLPsQ5F13J1h2TrUuSJGnRaRLIkiylF8YuraotAFV1a1UdqKp7gbcAT5ytb1VdXFVrqmrN8uXLh1f0GNq6Y5KNW3YyuW+KAib3TbFxy05DmSRJC6zFVZYB3gp8oap+d0b7KTM2ex5w3bBr08E2b9vF1P4DB7VN7T/A5m27GlUkSdLi1OIqy3OAlwI7k1zTtb0OOC/J2UABNwEvb1CbZtizb+qw2iVJ0pFpcZXlp4DMsurDw65F81uxbILJWcLXimUTDaqRJGnx8k79mtOGtauYWLrkoLaJpUvYsHZVo4okSVqcWgxZ6hixbnXv9nCbt+1iz74pViybYMPaVfe1S5KkhWEg07zWrV5pAJMkacAcspQkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1NhxrQvQ4rN1xySbt+1iz74pViybYMPaVaxbvbJ1WZIkjSwDmRbU1h2TbNyyk6n9BwCY3DfFxi07AQxlkiTNwSFLLajN23bdF8amTe0/wOZtuxpVJEnS6DOQaUHt2Td1WO2SJMlApgW2YtnEYbVLkiQDmRbYhrWrmFi65KC2iaVL2LB2VaOKJEkafU7q14KanrjvVZaSJPXPQKYFt271SgOYJEmHwSFLSZKkxgxkkiRJjTlkqWOWTwSQJC0WBjIdk3wigCRpMTGQ6Zg03xMB5gtknlWTJI0iA5mOSUfyRADPqkmSRpWT+nVMOpInAvicTUnSqDKQ6Zh0JE8E8DmbkqRRZSDTMWnd6pW84fmPZ+WyCQKsXDbBG57/+HmHHn3OpiRpVDmHTMesw30iwIa1qw6aQwY+Z1OSNBoMZBobPmdTkjSqDGQaKz5nU5I0ipxDJkmS1JhnyDQSvGGrJGmcGcjUnDdslSSNOwOZmjvSxyAtNp4llKTxZSBTc6N+w9ZhBCXPEkrSeHNSv5ob5Ru2TgelyX1TFP8SlLbumFzQ7/GxTpI03gxkau5IHoM0LMMKSqN+llCSNFgGMjV3JI9BGpZhBaVRPksoSRo855BpJIzqDVtXLJtgcpbwtdBB6Ugf6+SFAJK0OIzcGbIkz0iyK8kNSS5oXY/G25EOp27dMck5F13JmRd8iHMuuvJ+55wdyVnCYc1vkyQNXqqqdQ33SbIE+Efgp4DdwGeB86rq87Ntv2bNmtq+ffsQK9Q4OtyzUIdeMQm9ELfQw7DnXHTlrGfvVi6b4NMXPHXe+g73rJp97GMf+9jn6CW5uqrWzLpuxALZk4BNVbW2W94IUFVvmG17A5lG0ZEGpcN15gUfYrb/egN85aKfnbXPkYRF+9jHPvaxz8KYL5CN2pDlSuBrM5Z3d23SMWOULwQ4kqtG7WMf+9jHPoM3aoEss7QddBIgyfok25Ns37t375DKkvo3rCsmj2R+25GERfvYxz72sc/gjVog2w2cNmP5VGDPzA2q6uKqWlNVa5YvXz7U4qR+DOu+akdyIcCRhEX72Mc+9rHP4I1aIPsscFaSM5M8AHgR8IHGNUmHZZj3VVu3eiWfvuCpfOWin+XTFzz1fr/jSMKifexjH/vYZ/CWbNq0aehfOpdNmzbde+GFF34JuBT4VeBdVXX5XNtffPHFm9avXz+0+qR+PfqUk3jZk8/kVU9/FC978pk8+pSTWpcE9Oo69cET7Jz8Jnd99x5WLpvgN5/9mHmDnH3sYx/72GdhXHjhhbds2rTp4tnWjdRVlofLqywlSdKx4li6ylKSJGnsGMgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmOpqtY1HLEke4Gbh/BVJwP/NITvGWXuA/cBuA/AfQDuA3AfgPsADn8fPLyqls+24pgOZMOSZHtVrWldR0vuA/cBuA/AfQDuA3AfgPsAFnYfOCEQ4bsAAAX0SURBVGQpSZLUmIFMkiSpMQNZfy5uXcAIcB+4D8B9AO4DcB+A+wDcB7CA+8A5ZJIkSY15hkySJKkxA9k8kjwjya4kNyS5oHU9LSS5KcnOJNck2d66nmFJ8rYktyW5bkbbQ5J8NMmXutcHt6xx0ObYB5uSTHbHwzVJntmyxkFKclqSjyf5QpLrk/x61z42x8E8+2CcjoPvT/KZJP/Q7YMLu/ZxOg7m2gdjcxxMS7IkyY4kf9ktL9hx4JDlHJIsAf4R+ClgN/BZ4Lyq+nzTwoYsyU3Amqoaq3vNJHkKcBfwjqp6XNf2O8AdVXVRF9AfXFW/0bLOQZpjH2wC7qqqN7asbRiSnAKcUlWfS3IicDWwDvgFxuQ4mGcfvIDxOQ4CHF9VdyVZCnwK+HXg+YzPcTDXPngGY3IcTEvyamANcFJVPWsh/13wDNncngjcUFU3VtU/A+8Bntu4Jg1JVV0F3HFI83OBS7r3l9D7h2nRmmMfjI2quqWqPte9vxP4ArCSMToO5tkHY6N67uoWl3Y/xXgdB3Ptg7GS5FTgZ4H/M6N5wY4DA9ncVgJfm7G8mzH7Q9Qp4CNJrk6yvnUxjT2sqm6B3j9UwEMb19PKK5Nc2w1pLtphmpmSnAGsBv6eMT0ODtkHMEbHQTdMdQ1wG/DRqhq742COfQBjdBwAvw+8Frh3RtuCHQcGsrlllrax+z8C4Jyq+jHgZ4Bf6YaxNL7eDDwSOBu4BXhT23IGL8kJwOXAq6rqW63raWGWfTBWx0FVHaiqs4FTgScmeVzrmoZtjn0wNsdBkmcBt1XV1YP6DgPZ3HYDp81YPhXY06iWZqpqT/d6G3AFvaHccXVrN6dmem7NbY3rGbqqurX7w3wv8BYW+fHQzZe5HLi0qrZ0zWN1HMy2D8btOJhWVfuAT9CbOzVWx8G0mftgzI6Dc4DndPOq3wM8Ncm7WMDjwEA2t88CZyU5M8kDgBcBH2hc01AlOb6byEuS44GfBq6bv9ei9gHg/O79+cD7G9bSxPQfns7zWMTHQzeR+a3AF6rqd2esGpvjYK59MGbHwfIky7r3E8DTgS8yXsfBrPtgnI6DqtpYVadW1Rn08sCVVfUSFvA4OO6oq1ykquqeJK8EtgFLgLdV1fWNyxq2hwFX9P4mcxzw7qr667YlDUeSy4BzgZOT7AZeD1wEvC/Jy4CvAj/frsLBm2MfnJvkbHrD9zcBL29W4OCdA7wU2NnNnQF4HeN1HMy1D84bo+PgFOCS7sr77wPeV1V/meT/MT7HwVz74J1jdBzMZcH+HnjbC0mSpMYcspQkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSjnlJ7upez0jy7xf4s193yPL/XcjPlyQwkElaXM4ADiuQdfdWms9Bgayq/s1h1iRJ98tAJmkxuQj4ySTXJPkv3QORNyf5bPcA5JcDJDk3yceTvBvY2bVtTXJ1kuuTrO/aLgImus+7tGubPhuX7rOvS7IzyQtnfPYnkvxFki8mubS7470kzck79UtaTC4A/mtVPQugC1bfrKofT/JA4NNJPtJt+0TgcVX1lW75l6rqju7RMJ9NcnlVXZDkld1DlQ/1fHoPVf5R4OSuz1XdutXAY+k9//bT9O54/6mF/3UlLRaeIZO0mP008B+6x/78PfCDwFndus/MCGMAv5bkH4C/A06bsd1cngxc1j1c+Vbgk8CPz/js3d1Dl6+hN5QqSXPyDJmkxSzAr1bVtoMak3OBbx+y/HTgSVX1nSSfAL6/j8+ey90z3h/Av7WS7odnyCQtJncCJ85Y3gb8cpKlAEkeleT4Wfr9APCNLow9GvjXM9btn+5/iKuAF3bz1JYDTwE+syC/haSx4/+1SVpMrgXu6YYe3w78Ab3hws91E+v3Autm6ffXwCuSXAvsojdsOe1i4Nokn6uqF89ovwJ4EvAPQAGvraqvd4FOkg5Lqqp1DZIkSWPNIUtJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSY/8ftt2/3UUkj5sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Use a five-layer Net to overfit 50 training examples.\n",
    "\n",
    "num_train = 50\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "learning_rate = 1e-3\n",
    "weight_scale = 1e-1\n",
    "model = FullyConnectedNet([100, 100, 100, 100],\n",
    "                weight_scale=weight_scale, dtype=np.float64)\n",
    "solver = Solver(model, small_data,\n",
    "                print_every=10, num_epochs=20, batch_size=25,\n",
    "                update_rule='sgd',\n",
    "                optim_config={\n",
    "                  'learning_rate': learning_rate,\n",
    "                }\n",
    "         )\n",
    "solver.train()\n",
    "\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.title('Training loss history')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Training loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inline question: \n",
    "Did you notice anything about the comparative difficulty of training the three-layer net vs training the five layer net?\n",
    "\n",
    "# Answer:\n",
    "[FILL THIS IN]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Update rules\n",
    "So far we have used vanilla stochastic gradient descent (SGD) as our update rule. More sophisticated update rules can make it easier to train deep networks. We will implement a few of the most commonly used update rules and compare them to vanilla SGD."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SGD+Momentum\n",
    "Stochastic gradient descent with momentum is a widely used update rule that tends to make deep networks converge faster than vanilla stochstic gradient descent.\n",
    "\n",
    "Open the file `cs231n/optim.py` and read the documentation at the top of the file to make sure you understand the API. Implement the SGD+momentum update rule in the function `sgd_momentum` and run the following to check your implementation. You should see errors less than 1e-8."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  8.882347033505819e-09\n",
      "velocity error:  4.269287743278663e-09\n"
     ]
    }
   ],
   "source": [
    "from cs231n.optim import sgd_momentum\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "v = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-3, 'velocity': v}\n",
    "next_w, _ = sgd_momentum(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [ 0.1406,      0.20738947,  0.27417895,  0.34096842,  0.40775789],\n",
    "  [ 0.47454737,  0.54133684,  0.60812632,  0.67491579,  0.74170526],\n",
    "  [ 0.80849474,  0.87528421,  0.94207368,  1.00886316,  1.07565263],\n",
    "  [ 1.14244211,  1.20923158,  1.27602105,  1.34281053,  1.4096    ]])\n",
    "expected_velocity = np.asarray([\n",
    "  [ 0.5406,      0.55475789,  0.56891579, 0.58307368,  0.59723158],\n",
    "  [ 0.61138947,  0.62554737,  0.63970526,  0.65386316,  0.66802105],\n",
    "  [ 0.68217895,  0.69633684,  0.71049474,  0.72465263,  0.73881053],\n",
    "  [ 0.75296842,  0.76712632,  0.78128421,  0.79544211,  0.8096    ]])\n",
    "\n",
    "print ('next_w error: ', rel_error(next_w, expected_next_w))\n",
    "print ('velocity error: ', rel_error(expected_velocity, config['velocity']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you have done so, run the following to train a six-layer network with both SGD and SGD+momentum. You should see the SGD+momentum update rule converge faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running with  sgd\n",
      "(Iteration 1 / 200) loss: 2.795152\n",
      "(Epoch 0 / 5) train acc: 0.098000; val_acc: 0.121000\n",
      "(Iteration 11 / 200) loss: 2.299029\n",
      "(Iteration 21 / 200) loss: 2.175528\n",
      "(Iteration 31 / 200) loss: 2.033577\n",
      "(Epoch 1 / 5) train acc: 0.280000; val_acc: 0.262000\n",
      "(Iteration 41 / 200) loss: 1.980773\n",
      "(Iteration 51 / 200) loss: 2.011857\n",
      "(Iteration 61 / 200) loss: 1.884740\n",
      "(Iteration 71 / 200) loss: 1.916866\n",
      "(Epoch 2 / 5) train acc: 0.330000; val_acc: 0.267000\n",
      "(Iteration 81 / 200) loss: 1.871763\n",
      "(Iteration 91 / 200) loss: 1.860773\n",
      "(Iteration 101 / 200) loss: 1.894492\n",
      "(Iteration 111 / 200) loss: 1.550322\n",
      "(Epoch 3 / 5) train acc: 0.388000; val_acc: 0.300000\n",
      "(Iteration 121 / 200) loss: 1.762479\n",
      "(Iteration 131 / 200) loss: 1.851201\n",
      "(Iteration 141 / 200) loss: 1.728096\n",
      "(Iteration 151 / 200) loss: 1.823284\n",
      "(Epoch 4 / 5) train acc: 0.398000; val_acc: 0.320000\n",
      "(Iteration 161 / 200) loss: 1.795004\n",
      "(Iteration 171 / 200) loss: 1.687165\n",
      "(Iteration 181 / 200) loss: 1.524628\n",
      "(Iteration 191 / 200) loss: 1.543905\n",
      "(Epoch 5 / 5) train acc: 0.420000; val_acc: 0.339000\n",
      "running with  sgd_momentum\n",
      "(Iteration 1 / 200) loss: 3.005058\n",
      "(Epoch 0 / 5) train acc: 0.131000; val_acc: 0.101000\n",
      "(Iteration 11 / 200) loss: 2.170647\n",
      "(Iteration 21 / 200) loss: 1.945880\n",
      "(Iteration 31 / 200) loss: 2.033914\n",
      "(Epoch 1 / 5) train acc: 0.304000; val_acc: 0.295000\n",
      "(Iteration 41 / 200) loss: 1.915101\n",
      "(Iteration 51 / 200) loss: 1.848966\n",
      "(Iteration 61 / 200) loss: 1.774532\n",
      "(Iteration 71 / 200) loss: 1.769607\n",
      "(Epoch 2 / 5) train acc: 0.375000; val_acc: 0.309000\n",
      "(Iteration 81 / 200) loss: 1.867082\n",
      "(Iteration 91 / 200) loss: 1.560424\n",
      "(Iteration 101 / 200) loss: 1.578071\n",
      "(Iteration 111 / 200) loss: 1.729807\n",
      "(Epoch 3 / 5) train acc: 0.444000; val_acc: 0.357000\n",
      "(Iteration 121 / 200) loss: 1.447208\n",
      "(Iteration 131 / 200) loss: 1.379842\n",
      "(Iteration 141 / 200) loss: 1.547021\n",
      "(Iteration 151 / 200) loss: 1.461664\n",
      "(Epoch 4 / 5) train acc: 0.509000; val_acc: 0.370000\n",
      "(Iteration 161 / 200) loss: 1.350014\n",
      "(Iteration 171 / 200) loss: 1.602148\n",
      "(Iteration 181 / 200) loss: 1.232553\n",
      "(Iteration 191 / 200) loss: 1.416653\n",
      "(Epoch 5 / 5) train acc: 0.506000; val_acc: 0.352000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-18-033eb6df765d>:39: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  plt.subplot(3, 1, 1)\n",
      "<ipython-input-18-033eb6df765d>:42: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  plt.subplot(3, 1, 2)\n",
      "<ipython-input-18-033eb6df765d>:45: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  plt.subplot(3, 1, 3)\n",
      "<ipython-input-18-033eb6df765d>:49: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  plt.subplot(3, 1, i)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_train = 4000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "solvers = {}\n",
    "\n",
    "for update_rule in ['sgd', 'sgd_momentum']:\n",
    "  print ('running with ', update_rule)\n",
    "  model = FullyConnectedNet([100, 100, 100, 100, 100], weight_scale=5e-2)\n",
    "\n",
    "  solver = Solver(model, small_data,\n",
    "                  num_epochs=5, batch_size=100,\n",
    "                  update_rule=update_rule,\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-2,\n",
    "                  },\n",
    "                  verbose=True)\n",
    "  solvers[update_rule] = solver\n",
    "  solver.train()\n",
    "  print\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "for update_rule, solver in solvers.items():\n",
    "  plt.subplot(3, 1, 1)\n",
    "  plt.plot(solver.loss_history, 'o', label=update_rule)\n",
    "  \n",
    "  plt.subplot(3, 1, 2)\n",
    "  plt.plot(solver.train_acc_history, '-o', label=update_rule)\n",
    "\n",
    "  plt.subplot(3, 1, 3)\n",
    "  plt.plot(solver.val_acc_history, '-o', label=update_rule)\n",
    "  \n",
    "for i in [1, 2, 3]:\n",
    "  plt.subplot(3, 1, i)\n",
    "  plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RMSProp and Adam\n",
    "RMSProp [1] and Adam [2] are update rules that set per-parameter learning rates by using a running average of the second moments of gradients.\n",
    "\n",
    "In the file `cs231n/optim.py`, implement the RMSProp update rule in the `rmsprop` function and implement the Adam update rule in the `adam` function, and check your implementations using the tests below.\n",
    "\n",
    "[1] Tijmen Tieleman and Geoffrey Hinton. \"Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude.\" COURSERA: Neural Networks for Machine Learning 4 (2012).\n",
    "\n",
    "[2] Diederik Kingma and Jimmy Ba, \"Adam: A Method for Stochastic Optimization\", ICLR 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  9.524687511038133e-08\n",
      "cache error:  2.6477955807156126e-09\n"
     ]
    }
   ],
   "source": [
    "# Test RMSProp implementation; you should see errors less than 1e-7\n",
    "from cs231n.optim import rmsprop\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "cache = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-2, 'cache': cache}\n",
    "next_w, _ = rmsprop(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [-0.39223849, -0.34037513, -0.28849239, -0.23659121, -0.18467247],\n",
    "  [-0.132737,   -0.08078555, -0.02881884,  0.02316247,  0.07515774],\n",
    "  [ 0.12716641,  0.17918792,  0.23122175,  0.28326742,  0.33532447],\n",
    "  [ 0.38739248,  0.43947102,  0.49155973,  0.54365823,  0.59576619]])\n",
    "expected_cache = np.asarray([\n",
    "  [ 0.5976,      0.6126277,   0.6277108,   0.64284931,  0.65804321],\n",
    "  [ 0.67329252,  0.68859723,  0.70395734,  0.71937285,  0.73484377],\n",
    "  [ 0.75037008,  0.7659518,   0.78158892,  0.79728144,  0.81302936],\n",
    "  [ 0.82883269,  0.84469141,  0.86060554,  0.87657507,  0.8926    ]])\n",
    "\n",
    "print ('next_w error: ', rel_error(expected_next_w, next_w))\n",
    "print ('cache error: ', rel_error(expected_cache, config['cache']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  0.20720703668629928\n",
      "v error:  4.208314038113071e-09\n",
      "m error:  4.214963193114416e-09\n"
     ]
    }
   ],
   "source": [
    "# Test Adam implementation; you should see errors around 1e-7 or less\n",
    "from cs231n.optim import adam\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "m = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "v = np.linspace(0.7, 0.5, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-2, 'm': m, 'v': v, 't': 5}\n",
    "next_w, _ = adam(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [-0.40094747, -0.34836187, -0.29577703, -0.24319299, -0.19060977],\n",
    "  [-0.1380274,  -0.08544591, -0.03286534,  0.01971428,  0.0722929],\n",
    "  [ 0.1248705,   0.17744702,  0.23002243,  0.28259667,  0.33516969],\n",
    "  [ 0.38774145,  0.44031188,  0.49288093,  0.54544852,  0.59801459]])\n",
    "expected_v = np.asarray([\n",
    "  [ 0.69966,     0.68908382,  0.67851319,  0.66794809,  0.65738853,],\n",
    "  [ 0.64683452,  0.63628604,  0.6257431,   0.61520571,  0.60467385,],\n",
    "  [ 0.59414753,  0.58362676,  0.57311152,  0.56260183,  0.55209767,],\n",
    "  [ 0.54159906,  0.53110598,  0.52061845,  0.51013645,  0.49966,   ]])\n",
    "expected_m = np.asarray([\n",
    "  [ 0.48,        0.49947368,  0.51894737,  0.53842105,  0.55789474],\n",
    "  [ 0.57736842,  0.59684211,  0.61631579,  0.63578947,  0.65526316],\n",
    "  [ 0.67473684,  0.69421053,  0.71368421,  0.73315789,  0.75263158],\n",
    "  [ 0.77210526,  0.79157895,  0.81105263,  0.83052632,  0.85      ]])\n",
    "\n",
    "print ('next_w error: ', rel_error(expected_next_w, next_w))\n",
    "print ('v error: ', rel_error(expected_v, config['v']))\n",
    "print ('m error: ', rel_error(expected_m, config['m']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you have debugged your RMSProp and Adam implementations, run the following to train a pair of deep networks using these new update rules:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running with  adam\n",
      "(Iteration 1 / 200) loss: 2.520903\n",
      "(Epoch 0 / 5) train acc: 0.141000; val_acc: 0.147000\n",
      "(Iteration 11 / 200) loss: 2.259673\n",
      "(Iteration 21 / 200) loss: 2.064752\n",
      "(Iteration 31 / 200) loss: 1.965916\n",
      "(Epoch 1 / 5) train acc: 0.302000; val_acc: 0.271000\n",
      "(Iteration 41 / 200) loss: 2.071985\n",
      "(Iteration 51 / 200) loss: 1.897769\n",
      "(Iteration 61 / 200) loss: 1.708925\n",
      "(Iteration 71 / 200) loss: 1.731292\n",
      "(Epoch 2 / 5) train acc: 0.323000; val_acc: 0.299000\n",
      "(Iteration 81 / 200) loss: 1.708893\n",
      "(Iteration 91 / 200) loss: 1.778435\n",
      "(Iteration 101 / 200) loss: 1.779276\n",
      "(Iteration 111 / 200) loss: 1.580616\n",
      "(Epoch 3 / 5) train acc: 0.367000; val_acc: 0.327000\n",
      "(Iteration 121 / 200) loss: 1.609835\n",
      "(Iteration 131 / 200) loss: 1.669736\n",
      "(Iteration 141 / 200) loss: 1.604920\n",
      "(Iteration 151 / 200) loss: 1.682734\n",
      "(Epoch 4 / 5) train acc: 0.420000; val_acc: 0.349000\n",
      "(Iteration 161 / 200) loss: 1.720694\n",
      "(Iteration 171 / 200) loss: 1.765826\n",
      "(Iteration 181 / 200) loss: 1.657066\n",
      "(Iteration 191 / 200) loss: 1.373337\n",
      "(Epoch 5 / 5) train acc: 0.443000; val_acc: 0.368000\n",
      "running with  rmsprop\n",
      "(Iteration 1 / 200) loss: 2.591494\n",
      "(Epoch 0 / 5) train acc: 0.087000; val_acc: 0.111000\n",
      "(Iteration 11 / 200) loss: 2.106812\n",
      "(Iteration 21 / 200) loss: 2.035070\n",
      "(Iteration 31 / 200) loss: 1.990275\n",
      "(Epoch 1 / 5) train acc: 0.355000; val_acc: 0.305000\n",
      "(Iteration 41 / 200) loss: 1.883012\n",
      "(Iteration 51 / 200) loss: 1.742189\n",
      "(Iteration 61 / 200) loss: 1.699420\n",
      "(Iteration 71 / 200) loss: 1.625755\n",
      "(Epoch 2 / 5) train acc: 0.410000; val_acc: 0.299000\n",
      "(Iteration 81 / 200) loss: 1.713505\n",
      "(Iteration 91 / 200) loss: 1.742997\n",
      "(Iteration 101 / 200) loss: 1.552436\n",
      "(Iteration 111 / 200) loss: 1.663421\n",
      "(Epoch 3 / 5) train acc: 0.450000; val_acc: 0.338000\n",
      "(Iteration 121 / 200) loss: 1.519035\n",
      "(Iteration 131 / 200) loss: 1.393904\n",
      "(Iteration 141 / 200) loss: 1.581204\n",
      "(Iteration 151 / 200) loss: 1.411593\n",
      "(Epoch 4 / 5) train acc: 0.459000; val_acc: 0.350000\n",
      "(Iteration 161 / 200) loss: 1.458389\n",
      "(Iteration 171 / 200) loss: 1.635106\n",
      "(Iteration 181 / 200) loss: 1.508990\n",
      "(Iteration 191 / 200) loss: 1.407788\n",
      "(Epoch 5 / 5) train acc: 0.547000; val_acc: 0.362000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-21-3ed7cb34feb6>:30: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  plt.subplot(3, 1, 1)\n",
      "<ipython-input-21-3ed7cb34feb6>:33: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  plt.subplot(3, 1, 2)\n",
      "<ipython-input-21-3ed7cb34feb6>:36: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  plt.subplot(3, 1, 3)\n",
      "<ipython-input-21-3ed7cb34feb6>:40: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  plt.subplot(3, 1, i)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learning_rates = {'rmsprop': 1e-4, 'adam': 1e-3}\n",
    "for update_rule in ['adam', 'rmsprop']:\n",
    "  print ('running with ', update_rule)\n",
    "  model = FullyConnectedNet([100, 100, 100, 100, 100], weight_scale=5e-2)\n",
    "\n",
    "  solver = Solver(model, small_data,\n",
    "                  num_epochs=5, batch_size=100,\n",
    "                  update_rule=update_rule,\n",
    "                  optim_config={\n",
    "                    'learning_rate': learning_rates[update_rule]\n",
    "                  },\n",
    "                  verbose=True)\n",
    "  solvers[update_rule] = solver\n",
    "  solver.train()\n",
    "  print\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "for update_rule, solver in solvers.items():\n",
    "  plt.subplot(3, 1, 1)\n",
    "  plt.plot(solver.loss_history, 'o', label=update_rule)\n",
    "  \n",
    "  plt.subplot(3, 1, 2)\n",
    "  plt.plot(solver.train_acc_history, '-o', label=update_rule)\n",
    "\n",
    "  plt.subplot(3, 1, 3)\n",
    "  plt.plot(solver.val_acc_history, '-o', label=update_rule)\n",
    "  \n",
    "for i in [1, 2, 3]:\n",
    "  plt.subplot(3, 1, i)\n",
    "  plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train a good model!\n",
    "Train the best fully-connected model that you can on CIFAR-10, storing your best model in the `best_model` variable. We require you to get at least 50% accuracy on the validation set using a fully-connected net.\n",
    "\n",
    "If you are careful it should be possible to get accuracies above 55%, but we don't require it for this part and won't assign extra credit for doing so. Later in the assignment we will ask you to train the best convolutional network that you can on CIFAR-10, and we would prefer that you spend your effort working on convolutional nets rather than fully-connected nets.\n",
    "\n",
    "You might find it useful to complete the `BatchNormalization.ipynb` and `Dropout.ipynb` notebooks before completing this part, since those techniques can help you train powerful models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 2450) loss: 2.302044\n",
      "(Epoch 0 / 5) train acc: 0.141000; val_acc: 0.128000\n",
      "(Epoch 1 / 5) train acc: 0.533000; val_acc: 0.472000\n",
      "(Iteration 501 / 2450) loss: 1.378807\n",
      "(Epoch 2 / 5) train acc: 0.555000; val_acc: 0.472000\n",
      "(Iteration 1001 / 2450) loss: 1.321359\n",
      "(Epoch 3 / 5) train acc: 0.604000; val_acc: 0.521000\n",
      "(Iteration 1501 / 2450) loss: 1.132524\n",
      "(Epoch 4 / 5) train acc: 0.648000; val_acc: 0.549000\n",
      "(Iteration 2001 / 2450) loss: 0.939017\n",
      "(Epoch 5 / 5) train acc: 0.663000; val_acc: 0.524000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-22-fffee823c2b3>:35: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  plt.subplot(3, 1, 1)\n",
      "<ipython-input-22-fffee823c2b3>:38: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  plt.subplot(3, 1, 2)\n",
      "<ipython-input-22-fffee823c2b3>:41: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  plt.subplot(3, 1, 3)\n",
      "<ipython-input-22-fffee823c2b3>:45: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  plt.subplot(3, 1, i)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_model = None\n",
    "################################################################################\n",
    "# TODO: Train the best FullyConnectedNet that you can on CIFAR-10. You might   #\n",
    "# batch normalization and dropout useful. Store your best model in the         #\n",
    "# best_model variable.                                                         #\n",
    "################################################################################\n",
    "pass\n",
    "weight_scale = 2e-2\n",
    "learning_rate = 1e-4\n",
    "update_rule='rmsprop'\n",
    "model = FullyConnectedNet([600, 500, 400, 300, 200], weight_scale=weight_scale)\n",
    "\n",
    "solver = Solver(model, data,\n",
    "                print_every=500, num_epochs=5, batch_size=100,\n",
    "                update_rule='rmsprop',\n",
    "                optim_config={\n",
    "                'learning_rate': learning_rate\n",
    "                },\n",
    "                lr_decay=0.9,\n",
    "                verbose=True)\n",
    "solver.train()\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.plot(solver.loss_history, label=update_rule)\n",
    "  \n",
    "plt.subplot(3, 1, 2)\n",
    "plt.plot(solver.train_acc_history, '-o', label=update_rule)\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.plot(solver.val_acc_history, '-o', label=update_rule)\n",
    "  \n",
    "for i in [1, 2, 3]:\n",
    "  plt.subplot(3, 1, i)\n",
    "  plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()\n",
    "\n",
    "best_model = model\n",
    "################################################################################\n",
    "#                              END OF YOUR CODE                                #\n",
    "################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test you model\n",
    "Run your best model on the validation and test sets. You should achieve above 50% accuracy on the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation set accuracy:  0.549\n",
      "Test set accuracy:  0.567\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = np.argmax(best_model.loss(data['X_test']), axis=1)\n",
    "y_val_pred = np.argmax(best_model.loss(data['X_val']), axis=1)\n",
    "print('Validation set accuracy: ', (y_val_pred == data['y_val']).mean())\n",
    "print('Test set accuracy: ', (y_test_pred == data['y_test']).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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